预测不可预测之事：基于双向LSTM的全球恐怖主义数据库（GTD）事件计数可复现性预测

Predicting the Unpredictable: Reproducible BiLSTM Forecasting of Incident Counts in the Global Terrorism Database (GTD)

October 16, 2025

作者: Oluwasegun Adegoke

cs.AI

摘要

我们利用全球恐怖主义数据库（GTD，1970-2016）对每周恐怖主义事件数量进行短期预测研究。我们构建了一个可重复的流程，采用固定时间划分，并将双向长短期记忆网络（BiLSTM）与强基准模型（季节性朴素模型、线性/ARIMA模型）以及深度LSTM-注意力基线模型进行对比评估。在保留的测试集上，BiLSTM取得了6.38的均方根误差（RMSE），优于LSTM-注意力模型（9.19；提升30.6%）和线性滞后回归基线模型（RMSE提升35.4%），同时在平均绝对误差（MAE）和平均绝对百分比误差（MAPE）上也有并行改进。通过消融实验，我们考察了时间记忆、训练历史长度、空间粒度、回望窗口大小及特征组的变化，发现基于长期历史数据训练的模型泛化能力最佳；适中的回望窗口（20-30周）能提供强有力的上下文信息；双向编码对于捕捉窗口内的酝酿与后续模式至关重要。特征组分析表明，短期结构（滞后计数和滚动统计）贡献最大，地理和伤亡特征则带来增量提升。我们公开了代码、配置及简洁的结果表格，并提供了数据/伦理声明，记录了GTD的许可及仅用于研究的使用情况。总体而言，本研究为GTD事件预测提供了一个透明且超越基线的参考框架。

English

We study short-horizon forecasting of weekly terrorism incident counts using the Global Terrorism Database (GTD, 1970--2016). We build a reproducible pipeline with fixed time-based splits and evaluate a Bidirectional LSTM (BiLSTM) against strong classical anchors (seasonal-naive, linear/ARIMA) and a deep LSTM-Attention baseline. On the held-out test set, the BiLSTM attains RMSE 6.38, outperforming LSTM-Attention (9.19; +30.6\%) and a linear lag-regression baseline (+35.4\% RMSE gain), with parallel improvements in MAE and MAPE. Ablations varying temporal memory, training-history length, spatial grain, lookback size, and feature groups show that models trained on long historical data generalize best; a moderate lookback (20--30 weeks) provides strong context; and bidirectional encoding is critical for capturing both build-up and aftermath patterns within the window. Feature-group analysis indicates that short-horizon structure (lagged counts and rolling statistics) contributes most, with geographic and casualty features adding incremental lift. We release code, configs, and compact result tables, and provide a data/ethics statement documenting GTD licensing and research-only use. Overall, the study offers a transparent, baseline-beating reference for GTD incident forecasting.